[1]
Interface Design Optimization as a Multi-Armed Bandit Problem
Making Interfaces Work for Each Individual
/
Lomas, J. Derek
/
Forlizzi, Jodi
/
Poonwala, Nikhil
/
Patel, Nirmal
/
Shodhan, Sharan
/
Patel, Kishan
/
Koedinger, Ken
/
Brunskill, Emma
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.4142-4153
© Copyright 2016 ACM
Summary: "Multi-armed bandits" offer a new paradigm for the AI-assisted design of
user interfaces. To help designers understand the potential, we present the
results of two experimental comparisons between bandit algorithms and random
assignment. Our studies are intended to show designers how bandits algorithms
are able to rapidly explore an experimental design space and automatically
select the optimal design configuration. Our present focus is on the
optimization of a game design space. The results of our experiments show that
bandits can make data-driven design more efficient and accessible to interface
designers, but that human participation is essential to ensure that AI systems
optimize for the right metric. Based on our results, we introduce several
design lessons that help keep human design judgment in the loop. We also
consider the future of human-technology teamwork in AI-assisted design and
scientific inquiry. Finally, as bandits deploy fewer low-performing conditions
than typical experiments, we discuss ethical implications for bandits in
large-scale experiments in education.
[2]
Learning from Mixed-Reality Games: Is Shaking a Tablet as Effective as
Physical Observation?
Kids Haptic, Wearable, Tangible Learning
/
Yannier, Nesra
/
Koedinger, Kenneth R.
/
Hudson, Scott E.
Proceedings of the ACM CHI'15 Conference on Human Factors in Computing
Systems
2015-04-18
v.1
p.1045-1054
© Copyright 2015 ACM
Summary: The possibility of leveraging technology to support children's learning in
the real world is both appealing and technically challenging. We have been
exploring factors in tangible games that may contribute to both learning and
enjoyment with an eye toward technological feasibility and scalability.
Previous research found that young children learned early physics principles
better when interactively predicting and observing experimental comparisons on
a physical earthquake table than when seeing a video of the same. Immersing
children in the real world with computer vision-based feedback appears to evoke
embodied cognition that enhances learning. In the current experiment, we
replicated this intriguing result of the mere difference between observing the
real world versus a flat-screen. Further, we explored whether a simple and
scalable addition of physical control (such as shaking a tablet) would yield an
increase in learning and enjoyment. Our 2x2 experiment found no evidence that
adding simple forms of hands-on control enhances learning, while demonstrating
a large impact of physical observation. A general implication for educational
game design is that affording physical observation in the real world
accompanied by interactive feedback may be more important than affording simple
hands-on control on a tablet.
[3]
Optimizing challenge in an educational game using large-scale design
experiments
Papers: learning
/
Lomas, Derek
/
Patel, Kishan
/
Forlizzi, Jodi L.
/
Koedinger, Kenneth R.
Proceedings of ACM CHI 2013 Conference on Human Factors in Computing Systems
2013-04-27
v.1
p.89-98
© Copyright 2013 ACM
Summary: Online games can serve as research instruments to explore the effects of
game design elements on motivation and learning. In our research, we
manipulated the design of an online math game to investigate the effect of
challenge on player motivation and learning. To test the "Inverted-U
Hypothesis", which predicts that maximum game engagement will occur with
moderate challenge, we produced two large-scale (10K and 70K subjects),
multi-factor (2x3 and 2x9x8x4x25) online experiments. We found that, in almost
all cases, subjects were more engaged and played longer when the game was
easier, which seems to contradict the generality of the Inverted-U Hypothesis.
Troublingly, we also found that the most engaging design conditions produced
the slowest rates of learning. Based on our findings, we describe several
design implications that may increase challenge-seeking in games, such as
providing feedforward about the anticipated degree of challenge.
[4]
A paradigm for handwriting-based intelligent tutors
/
Anthony, Lisa
/
Yang, Jie
/
Koedinger, Kenneth R.
International Journal of Human-Computer Studies
2012-11
v.70
n.11
p.866-887
Keywords: Intelligent tutoring systems
Keywords: Pen input
Keywords: Handwriting recognition
Keywords: Mathematics
Keywords: Cognitive tutors
Keywords: Interaction design
Keywords: Human-computer interaction
Keywords: Educational technology
© Copyright 2012 Elsevier Ltd.
Summary: This paper presents the interaction design of, and demonstration of
technical feasibility for, intelligent tutoring systems that can accept
handwriting input from students. Handwriting and pen input offer several
affordances for students that traditional typing-based interactions do not. To
illustrate these affordances, we present evidence, from tutoring mathematics,
that the ability to enter problem solutions via pen input enables students to
record algebraic equations more quickly, more smoothly (fewer errors), and with
increased transfer to non-computer-based tasks. Furthermore our evidence shows
that students tend to like pen input for these types of problems more than
typing. However, a clear downside to introducing handwriting input into
intelligent tutors is that the recognition of such input is not reliable. In
our work, we have found that handwriting input is more likely to be useful and
reliable when context is considered, for example, the context of the problem
being solved. We present an intelligent tutoring system for algebra equation
solving via pen-based input that is able to use context to decrease recognition
errors by 18% and to reduce recognition error recovery interactions to occur on
one out of every four problems. We applied user-centered design principles to
reduce the negative impact of recognition errors in the following ways: (1)
though students handwrite their problem-solving process, they type their final
answer to reduce ambiguity for tutoring purposes, and (2) in the small number
of cases in which the system must involve the student in recognition error
recovery, the interaction focuses on identifying the student's problem-solving
error to keep the emphasis on tutoring. Many potential recognition errors can
thus be ignored and distracting interactions are avoided. This work can inform
the design of future systems for students using pen and sketch input for math
or other topics by motivating the use of context and pragmatics to decrease the
impact of recognition errors and put user focus on the task at hand.
[5]
User Modeling -- A Notoriously Black Art
Full Research Papers
/
Yudelson, Michael
/
Pavlik, Philip I.
/
Koedinger, Kenneth R.
Proceedings of the 2011 Conference on User Modeling, Adaptation and
Personalization
2011-07-11
p.317-328
Keywords: User modeling; educational data mining; model selection; model complexity;
model parsimony
© Copyright 2011 Springer-Verlag
Summary: This paper is intended as guidance for those who are familiar with user
modeling field but are less fluent in statistical methods. It addresses
potential problems with user model selection and evaluation, that are often
clear to expert modelers, but are not obvious for others. These problems are
frequently a result of a falsely straightforward application of statistics to
user modeling (e.g. over-reliance on model fit metrics). In such cases,
absolute trust in arguably shallow model accuracy measures could lead to
selecting models that are hard-to-interpret, less meaningful, over-fit, and
less generalizable. We offer a list of questions to consider in order to avoid
these modeling pitfalls. Each of the listed questions is backed by an
illustrative example based on the user modeling approach called Performance
Factors Analysis (PFA) [9].
[6]
INTERNET
Human-Computer Interaction Institute (HCII)
/
Aleven, Vincent
/
Anderson, John
/
Atkeson, Chris
/
Boyarski, Daniel
/
Cassell, Justine
/
Corbett, Albert
/
Dabbish, Laura
/
Date, Jenna
/
Dey, Anind
/
Evenson, Shelley
/
Forlizzi, Jodi
/
Hong, Jason
/
Hudson, Scott
/
John, Bonnie
/
Kam, Matthew
/
Kiesler, Sara
/
Kittur, Aniket
/
Klatzky, Roberta
/
Koedinger, Ken
/
Kraut, Robert
/
Lindqvist, Janne
/
Matsuda, Noboru
/
McLaren, Bruce M.
/
Morris, James
/
Myers, Brad
/
Neuwirth, Christine
/
Paulos, Eric
/
Pavlik, Philip I., Jr.
/
Rosé, Carolyn Penstein
/
Scheines, Richard
/
Siewiorek, Daniel P.
/
Stamper, John
/
Waibel, Alexander
/
Yang, Jie
/
Zimmerman, John
2010-08-26
2001-09-06
1998-05-22
United States, Pennsylvania, Pittsburgh
Carnegie Mellon University
[7]
Designing a pen-based flashcard application to support classroom learning
environment
Session: cooking, classrooms, and craft
/
Jeong, YoungJoo
/
Gunawardena, Ananda
/
Koedinger, Kenneth R.
Proceedings of ACM CHI 2010 Conference on Human Factors in Computing Systems
2010-04-10
v.2
p.4695-4698
Keywords: human-centered design, information interfaces and presentation, pen and
tactile input, pen-based uis and education, user-centered design
© Copyright 2010 ACM
Summary: Pen-based Flash Cards Application ("application") offers the flexibility of
handwritten input while benefiting a wide set of users to increase their memory
retention. It is particularly useful in learning mathematics where typing the
material using a keyboard can be difficult. In this study, we describe the
observations and major findings in a two-year case study in an eighth-grade
geometry class. We found that this application may enhance teacher-student
interaction, increase autonomy in students for self-guided learning, and
encourage collaborative learning.
[8]
Note-taking, selecting, and choice: designing interfaces that encourage
smaller selections
Interfaces and navigation
/
Bauer, Aaron
/
Koedinger, Kenneth R.
JCDL'08: Proceedings of the 8th ACM/IEEE-CS Joint Conference on Digital
Libraries
2008-06-16
p.397-406
© Copyright 2008 ACM
Summary: Our research develops note-taking applications for educational environments.
Previous studies found that while copy-pasting notes can be more efficient than
typing, for some users it reduces attention and learning. This paper presents
two studies aimed at designing and evaluating interfaces that encourage
focusing. While we were able to produce interfaces that increased desirable
behaviors and improved satisfaction, the new interfaces did not improve
learning. We suggest design recommendations derived from these studies, and
describe a "selecting-to-read" behavior we encountered, which has implications
for the design of reading and note-taking applications.
[9]
What's in a Step? Toward General, Abstract Representations of Tutoring
System Log Data
Poster Papers
/
VanLehn, Kurt
/
Koedinger, Kenneth R.
/
Skogsholm, Alida
/
Nwaigwe, Adaeze
Proceedings of User Modeling 2007
2007-07-25
p.455-459
Keywords: Student modeling; educational data mining; tutoring systems
© Copyright 2007 Springer-Verlag
Summary: The Pittsburgh Science of Learning Center (PSLC) is developing a data
storage and analysis facility, called DataShop. It currently handles log data
from 6 full-year tutoring systems and dozens of smaller, experimental tutoring
systems. DataShop requires a representation of log data that supports a variety
of tutoring systems, atheoretical analyses and theoretical analyses. The
theory-based analyses are strongly related to student modeling, so the lessons
learned in developing the DataShop's representation may apply to student
modeling in general. This report discusses the representation originally used
by the DataShop, the problems encountered, and how the key concept of "step"
evolved to meet these challenges.
[10]
Selection-based note-taking applications
Tags, tagging & notetaking
/
Bauer, Aaron
/
Koedinger, Kenneth R.
Proceedings of ACM CHI 2007 Conference on Human Factors in Computing Systems
2007-04-28
v.1
p.981-990
© Copyright 2007 ACM
Summary: The increasing integration of education and technology has led to the
development of a range of note-taking applications. Our project's goal is to
provide empirical data to guide the design of such note-taking applications by
evaluating the behavioral and learning outcomes of different note-taking
functionality. The study reported here compares note-taking using a text editor
and four interaction techniques. The two standard techniques are typing and
copy-paste. The two novel techniques are restricted copy-paste and
menu-selection, intended to increase attention and processing respectively.
Hypothesized learning gains from the novel techniques were not observed. As
implemented these techniques were less efficient and appeared to be more
frustrating to use. However, data regarding differences in both note-taking
efficiency and learning suggest several important implications for
selection-based note-taking applications, such as pasting and highlighting. Our
results also indicate that students have strong opinions regarding their
note-taking practices, which may complicate potentially beneficial
interventions.
[11]
Addressing the testing challenge with a web-based e-assessment system that
tutors as it assesses
E-learning & scientific applications
/
Feng, Mingyu
/
Heffernan, Neil T.
/
Koedinger, Kenneth R.
Proceedings of the 2006 International Conference on the World Wide Web
2006-05-23
p.307-316
Keywords: ASSISTment, MCAS, intelligent tutoring system, learning, predict
© Copyright 2006 International World Wide Web Conference Committee (IW3C2)
Summary: Secondary teachers across the country are being asked to use formative
assessment data to inform their classroom instruction. At the same time,
critics of No Child Left Behind are calling the bill "No Child Left Untested"
emphasizing the negative side of assessment, in that every hour spent assessing
students is an hour lost from instruction. Or does it have to be? What if we
better integrated assessment into the classroom, and we allowed students to
learn during the test? Maybe we could even provide tutoring on the steps of
solving problems. Our hypothesis is that we can achieve more accurate
assessment by not only using data on whether students get test items right or
wrong, but by also using data on the effort required for students to learn how
to solve a test item. We provide evidence for this hypothesis using data
collected with our E-ASSISTment system by more than 600 students over the
course of the 2004-2005 school year. We also show that we can track student
knowledge over time using modern longitudinal data analysis techniques. In a
separate paper [9], we report on the ASSISTment system's architecture and
scalability, while this paper is focused on how we can reliably assess student
learning.
[12]
Evaluating the effect of technology on note-taking and learning
Work-in-progress
/
Bauer, Aaron
/
Koedinger, Kenneth
Proceedings of ACM CHI 2006 Conference on Human Factors in Computing Systems
2006-04-22
v.2
p.520-525
© Copyright 2006 ACM
Summary: Current note-taking applications have been shown to affect the way students
take notes. The impact on learning has not been studied. In this paper, we
describe a project aimed at addressing how specific features of note-taking
tools impact both behavior and performance. We describe our initial results
evaluating copy-paste functionality, their implication for design, and future
studies. We believe this work has relevance not only for the design of
note-taking tools, but for a broader CHI audience.
[13]
Evaluation of multimodal input for entering mathematical equations on the
computer
Late breaking results: short papers
/
Anthony, Lisa
/
Yang, Jie
/
Koedinger, Kenneth R.
Proceedings of ACM CHI 2005 Conference on Human Factors in Computing Systems
2005-04-02
v.2
p.1184-1187
© Copyright 2005 ACM
Summary: Current standard interfaces for entering mathematical equations on computers
are arguably limited and cumbersome. Mathematics notations have evolved to aid
visual thinking and yet text-based interfaces relying on keyboard-and-mouse
input do not take advantage of the natural two-dimensional aspects of math. Due
to its similarities to paper-based mathematics, pen-based handwriting input may
be faster, more efficient, and more preferable for entering mathematics on
computers. This paper presents an empirical study that tests this hypothesis.
We also explored a multimodal input method combining handwriting and speech
because we hypothesize that it may enhance computer recognition and aid user
cognition. Novice users were indeed faster, more efficient and enjoyed the
handwriting modality more than a standard keyboard-and-mouse mathematics
interface, especially as equation length and complexity increased. The
multimodal handwriting-plus-speech method was faster and better liked than the
keyboard-and-mouse method and was not much worse than handwriting alone.
[14]
Off-task behavior in the cognitive tutor classroom: when students "game the
system"
/
Baker, Ryan Shaun
/
Corbett, Albert T.
/
Koedinger, Kenneth R.
/
Wagner, Angela Z.
Proceedings of ACM CHI 2004 Conference on Human Factors in Computing Systems
2004-04-24
v.1
p.383-390
© Copyright 2004 ACM
Summary: We investigate the prevalence and learning impact of different types of
off-task behavior in classrooms where students are using intelligent tutoring
software. We find that within the classrooms studied, no other type of off-task
behavior is associated nearly so strongly with reduced learning as "gaming the
system": behavior aimed at obtaining correct answers and advancing within the
tutoring curriculum by systematically taking advantage of regularities in the
software's feedback and help. A student's frequency of gaming the system
correlates as strongly to post-test score as the student's prior domain
knowledge and general academic achievement. Controlling for prior domain
knowledge, students who frequently game the system score substantially lower on
a post-test than students who never game the system. Analysis of students who
choose to game the system suggests that learned helplessness or performance
orientation might be better accounts for why students choose this behavior than
lack of interest in the material. This analysis will inform the future
re-design of tutors to respond appropriately when students game the system.
[15]
Predictive human performance modeling made easy
/
John, Bonnie E.
/
Prevas, Konstantine
/
Salvucci, Dario D.
/
Koedinger, Ken
Proceedings of ACM CHI 2004 Conference on Human Factors in Computing Systems
2004-04-24
v.1
p.455-462
© Copyright 2004 ACM
Summary: Although engineering models of user behavior have enjoyed a rich history in
HCI, they have yet to have a widespread impact due to the complexities of the
modeling process. In this paper we describe a development system in which
designers generate predictive cognitive models of user behavior simply by
demonstrating tasks on HTML mock-ups of new interfaces. Keystroke-Level Models
are produced automatically using new rules for placing mental operators, then
implemented in the ACT-R cognitive architecture. They interact with the mock-up
through integrated perceptual and motor modules, generating behavior that is
automatically quantified and easily examined. Using a query-entry user
interface as an example [19], we demonstrate that this new system enables more
rapid development of predictive models, with more accurate results, than
previously published models of these tasks.
[16]
Detecting When Students Game the System, Across Tutor Subjects and Classroom
Cohorts
Modeling and Recognizing Human Activity
/
Baker, Ryan Shaun
/
Corbett, Albert T.
/
Koedinger, Kenneth R.
/
Roll, Ido
Proceedings of User Modeling 2005
2003-07-24
p.220-224
© Copyright 2003 Springer-Verlag
Summary: Building a generalizable detector of student behavior within intelligent
tutoring systems presents two challenges: transferring between different
cohorts of students (who may develop idiosyncratic strategies of use), and
transferring between different tutor lessons (which may have considerable
variation in their interfaces, making cognitively equivalent behaviors appear
quite different within log files). In this paper, we present a machine-learned
detector which identifies students who are "gaming the system", attempting to
complete problems with minimal cognitive effort, and determine that the
detector transfers successfully across student cohorts but less successfully
across tutor lessons.
[17]
Modeling Students' Metacognitive Errors in Two Intelligent Tutoring Systems
Student Modeling
/
Roll, Ido
/
Baker, Ryan S.
/
Aleven, Vincent
/
McLaren, Bruce M.
/
Koedinger, Kenneth R.
Proceedings of User Modeling 2005
2003-07-24
p.367-376
© Copyright 2003 Springer-Verlag
Summary: Intelligent tutoring systems help students acquire cognitive skills by
tracing students' knowledge and providing relevant feedback. However, feedback
that focuses only on the cognitive level might not be optimal -- errors are
often the result of inappropriate metacognitive decisions. We have developed
two models which detect aspects of student faulty metacognitive behavior: A
prescriptive rational model aimed at improving help-seeking behavior, and a
descriptive machine-learned model aimed at eliminating attempts to "game" the
tutor. In a comparison between the two models we found that while both
successfully identify gaming behavior, one is better at characterizing the
types of problems students game in, and the other captures a larger variety of
faulty behaviors. An analysis of students' actions in two different tutors
suggests that the help-seeking model is domain independent, and that students'
behavior is fairly consistent across classrooms, age groups, domains, and task
elements.
[18]
A Comparative Analysis of Cognitive Tutoring and Constraint-Based Modeling
Student Modeling Methods
/
Mitrovic, Antonija
/
Koedinger, Kenneth R.
/
Martin, Brent
Proceedings of User Modeling 2003
2003-06-22
p.313-322
© Copyright 2003 Springer-Verlag
Summary: Numerous approaches to student modeling have been proposed since the
inception of the field more than three decades ago. What the field is lacking
completely is comparative analyses of different student modeling approaches. In
this paper we compare Cognitive Tutoring to Constraint-Based Modeling (CBM). We
present our experiences in implementing a database design tutor using both
methodologies and highlight their strengths and weaknesses. We compare their
characteristics and argue the differences are often more apparent than real:
for specific domains one approach may be favoured over the other, making them
viable complementary methods for supporting learning.
[19]
Third generation computer tutors: learn from or ignore human tutors?
Panel
/
Corbett, Albert
/
Anderson, John
/
Graesser, Art
/
Koedinger, Ken
/
VanLehn, Kurt
Proceedings of ACM CHI 99 Conference on Human Factors in Computing Systems
1999-05-15
v.2
p.85-86
© Copyright 1999 ACM
Summary: Current "second generation or "intelligent" computer tutors are
approximately one-half as effective as human tutors. How will we develop the
next generation of computer tutors that approaches human tutor effectiveness?
Does success lie in understanding and emulating the performance of human
tutors? If so, should we focus on natural language dialog or human tutor
pedagogy? Alternatively, does computer technology afford effective
instructional interventions, unavailable to human tutors? Can we modify
learning activities and monitor student problem solving in ways that human
tutors cannot.
[20]
EDITED BOOK
Handbook of Human-Computer Interaction
/
Helander, Martin
/
Landauer, Thomas K.
/
Prabhu, Prasad V.
1997
n.62
p.1582
Amsterdam
North-Holland
Elsevier Science Publishers
Second Edition
I Issues, Theories, Models and Methods in HCI
1 Human-Computer Interaction: Background and Issues
+ Nickerson, Raymond S.
+ Landauer, Thomas K.
2 Information Visualization
+ Hollan, James D.
+ Bederson, Benjamin B.
+ Helfman, Jonathan I.
3 Mental Models and User Models
+ Allen, Robert B.
4 Model-Based Optimization of Display Systems
+ Pavel, Misha
+ Ahumada, Albert J., Jr.
5 Task Analysis, Task Allocation and Supervisory Control
+ Sheridan, Thomas B.
6 Models of Graphical Perception
+ Lohse, Gerald Lee
7 Using Natural Language Interfaces
+ Ogden, William C.
+ Bernick, Philip
8 Virtual Environments as Human-Computer Interfaces
+ Ellis, Stephen R.
+ Begault, Durand R.
+ Wenzel, Elizabeth M.
9 Behavioral Research Methods in Human-Computer Interaction
+ Landauer, Thomas K.
II Design and Development of Software Systems
10 How To Design Usable Systems
+ Gould, John D.
+ Boies, Stephen J.
+ Ukelson, Jacob
11 Participatory Practices in the Software Lifecycle
+ Muller, Michael J.
+ Haslwanter, Jean Hallewell
+ Dayton, Tom
12 Design for Quality-in-use: Human-Computer Interaction Meets Information Systems Development
+ Ehn, Pelle
+ Lowgren, Jonas
13 Ecological Information Systems and Support of Learning: Coupling Work Domain Information to User Characteristics
+ Pejtersen, Annelise Mark
+ Rasmussen, Jens
14 The Role of Task Analysis in the Design of Software
+ Jeffries, Robin
15 The Use of Ethnographic Methods in Design and Evaluation
+ Nardi, Bonnie A.
16 What do Prototypes Prototype?
+ Houde, Stephanie
+ Hill, Charles
17 Scenario-Based Design
+ Carroll, John M.
18 International Ergonomic HCI Standards
+ Cakir, Ahmet
+ Dzida, Wolfgang
III User Interface Design
19 Graphical User Interfaces
+ Marcus, Aaron
20 The Role of Metaphors in User Interface Design
+ Neale, Dennis C.
+ Carroll, John M.
21 Direct Manipulation and Other Lessons
+ Frohlich, David M.
22 Human Error and User-Interface Design
+ Prabhu, Prasad V.
+ Prabhu, Girish V.
23 Screen Design
+ Tullis, Thomas S.
24 Design of Menus
+ Paap, Kenneth R.
+ Cooke, Nancy J.
25 Color and Human-Computer Interaction
+ Post, David L.
26 How Not to Have to Navigate Through Too Many Displays
+ Woods, David D.
+ Watts, Jennifer C.
IV Evaluation of HCI
27 The Usability Engineering Framework for Product Design and Evaluation
+ Wixon, Dennis
+ Wilson, Chauncey
28 User-Centered Software Evaluation Methodologies
+ Karat, John
29 Usability Inspection Methods
+ Virzi, Robert A.
30 Cognitive Walkthroughs
+ Lewis, Clayton
+ Wharton, Cathleen
31 A Guide to GOMS Model Usability Evaluation using NGOMSL
+ Kieras, David
32 Cost-Justifying Usability Engineering in the Software Life Cycle
+ Karat, Clare-Marie
V Individual Differences and Training
33 From Novice to Expert
+ Mayer, Richard E.
34 Computer Technology and the Older Adult
+ Czaja, Sara J.
35 Human Computer Interfaces for People with Disabilities
+ Newell, Alan F.
+ Gregor, Peter
36 Computer-Based Instruction
+ Eberts, Ray E.
37 Intelligent Tutoring Systems
+ Corbett, Albert T.
+ Koedinger, Kenneth R.
+ Anderson, John R.
VI Multimedia, Video and Voice
38 Hypertext and its Implications for the Internet
+ Vora, Pawan R.
+ Helander, Martin G.
39 Multimedia Interaction
+ Waterworth, John A.
+ Chignell, Mark H.
40 A Practical Guide to Working with Edited Video
+ Kellogg, Wendy A.
+ Bellamy, Rachel K. E.
+ Van Deusen, Mary
41 Desktop Video Conferencing: A Systems Approach
+ Kies, Jonathan K.
+ Williges, Robert C.
+ Williges, Beverly H.
42 Auditory Interfaces
+ Gaver, William W.
43 Design Issues for Interfaces using Voice Input
+ Kamm, Candace
+ Helander, Martin
44 Applying Speech Synthesis to User Interfaces
+ Spiegel, Murray F.
+ Streeter, Lynn
45 Designing Voice Menu Applications for Telephones
+ Marics, Monica A.
+ Engelbeck, George
VII Programming, Intelligent Interface Design and Knowledge-Based Systems
46 Expertise and Instruction in Software Development
+ Rosson, Mary Beth
+ Carroll, John M.
47 End-User Programming
+ Eisenberg, Michael
48 Interactive Software Architecture
+ Olsen, Dan R., Jr.
49 User Aspects Of Knowledge-Based Systems
+ Wærn, Yvonne
+ Hagglund, Sture
50 Paradigms for Intelligent Interface Design
+ Roth, Emilie M.
+ Malin, Jane T.
+ Schreckenghost, Debra L.
51 Knowledge Elicitation for the Design of Software Agents
+ Boy, Guy A.
52 Decision Support Systems: Integrating Decision Aiding And Decision Training
+ Zachary, Wayne W.
+ Ryder, Joan M.
53 Human Computer Interaction Applications for Intelligent Transportation Systems
+ Dingus, Thomas A.
+ Gellatly, Andrew W.
+ Reinach, Stephen J.
VIII Input Devices and Design of Work Stations
54 Keys and Keyboards
+ Lewis, James R.
+ Potosnak, Kathleen M.
+ Magyar, Regis L.
55 Pointing Devices
+ Greenstein, Joel S.
56 Ergonomics of CAD Systems
+ Luczak, Holger
+ Springer, Johannes
57 Design of the Computer Workstation
+ Kroemer, Karl H. E.
58 Work-related Disorders and the Operation of Computer VDT's
+ Hagberg, Mats
+ Rempel, David
IX CSCW and Organizational Issues in HCI
59 Research on Computer Supported Cooperative Work
+ Olson, Gary M.
+ Olson, Judith S.
60 Organizational Issues in Development and Implementation of Interactive Systems
+ Grudin, Jonathan
+ Markus, M. Lynne
61 Understanding the Organisational Ramifications of Implementing Information Technology Systems
+ Eason, Ken
62 Psychosocial Aspects of Computerized Office Work
+ Smith, Michael J.
+ Conway, Frank T.
